Object association method, apparatus and system, and storage medium

ABSTRACT

An object association method, apparatus and system, and a storage medium are provided. The method includes: obtaining a first image and a second image; and determining an association relationship between objects in the first image and objects in the second image based on surrounding information of the objects in the first image and surrounding information of the objects in the second image, where the surrounding information of one object is determined according to pixels within a set range around a bounding box of the object in the image where the object is located.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of International PatentApplication No. PCT/IB2020/060208, filed on Oct. 30, 2020, which claimspriority to Singapore Patent Application No. 10202007356U, filed on Aug.1, 2020. The disclosures of International Patent Application No.PCT/IB2020/060208 and Singapore Patent Application No. 10202007356U arehereby incorporated by reference in their entireties.

BACKGROUND

A multi-camera system is very common in real life. Compared with asingle-camera system, the multi-camera system has a wider field of view.For example, an unmanned aerial vehicle is generally equipped with amulti-camera system, so as to acquire images at different angles bymeans of the multi-camera system, and thus the shielding problem thatcannot be solved by a single camera can be solved. Complete informationof a photographed object can be obtained by fusing information ofcameras in the multi-camera system.

At present, multi-camera fusion can be performed by means of aconventional feature comparison method or a deep learning featurecomparison method. However, no matter which feature comparison method isused, objects having similar or same appearance cannot be distinguished.For a scene where a large number of same or similar objects areincluded, the use of a feature comparison method greatly reduces thefusion precision of information of the cameras in the multi-camerasystem.

SUMMARY

The present disclosure relates to, but is not limited to, imageprocessing technologies. Embodiments of the present disclosure providean object association method, apparatus and system, an electronicdevice, a storage medium and a computer program.

The technical solutions of the embodiments of the present disclosure areimplemented as follows:

An object association method provided by the embodiments of the presentdisclosure includes: obtaining a first image and a second image; anddetermining an association relationship between a plurality of objectsin the first image and a plurality of objects in the second image basedon surrounding information of the plurality of objects in the firstimage and surrounding information of the plurality of objects in thesecond image, where surrounding information of one object is determinedaccording to pixels within a set range around a bounding box of theobject in an image where the object is located.

The embodiments of the present disclosure also provide an objectassociation apparatus. The apparatus includes: a processor, and a memoryfor storing instructions executable by the processor, where theprocessor is configured to: obtain a first image and a second image; anddetermine an association relationship between a plurality of objects inthe first image and a plurality of objects in the second image based onsurrounding information of the plurality of objects in the first imageand surrounding information of the plurality of objects in the secondimage, where surrounding information of one object is determinedaccording to pixels within a set range around a bounding box of theobject in an image where the object is located.

The embodiments of the present disclosure also provide an objectassociation system. The system includes: a first image acquisitiondevice, configured to acquire one scene at a first view to obtain afirst image; a second image acquisition device, configured to acquirethe scene at a second view to obtain a second image, where the firstview is different from the second view; and a processor, configured toimplement the steps of the method as described above.

The embodiments of the present disclosure also provide a computerreadable storage medium, having a computer program stored thereon, wherethe program, when being executed by a processor, enables the processorto implement the steps of the method as described above.

In the object association method, apparatus and system, the electronicdevice, the storage medium and the computer program provided by theembodiments of the present disclosure, the method includes: obtaining afirst image and a second image; and determining an associationrelationship between objects in the first image and objects in thesecond image based on surrounding information of the objects in thefirst image and surrounding information of the objects in the secondimage, where the surrounding information of one object is determinedaccording to pixels within a set range around a bounding box of theobject in the image where the object is located. By using the technicalsolutions of the embodiments of the present disclosure, surroundinginformation of objects in different images is taken as the basis forassociation matching between the objects of the different images, sothat the association matching between objects having similar or sameappearance in two images are achieved, and the precision of associationmatching is improved.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic flowchart of an object association methodaccording to embodiments of the present disclosure.

FIG. 2 is another schematic flowchart of an object association methodaccording to embodiments of the present disclosure.

FIG. 3 is a schematic diagram of determining a feature distance in anobject association method according to embodiments of the presentdisclosure.

FIG. 4 is yet another schematic flowchart of an object associationmethod according to embodiments of the present disclosure.

FIG. 5 is a schematic diagram of determining a geometric distance in anobject association method according to embodiments of the presentdisclosure.

FIG. 6 is a schematic diagram of determining an association relationshipin an object association method according to embodiments of the presentdisclosure.

FIG. 7 is a schematic structural diagram of components of an objectassociation apparatus according to embodiments of the presentdisclosure.

FIG. 8 is a schematic structural diagram of hardware of an electronicdevice according to embodiments of the present disclosure.

DETAILED DESCRIPTION

The present disclosure is further described in detail below withreference to the accompanying drawings and the specific embodiments.

Embodiments of the present disclosure provide an object associationmethod. FIG. 1 is a schematic flowchart of an object association methodaccording to the embodiments of the present disclosure. As shown in FIG.1 , the method includes the following steps.

At step 101, a first image and a second image are obtained.

At step 102, an association relationship between objects in the firstimage and objects in the second image is determined based on surroundinginformation of the objects in the first image and surroundinginformation of the objects in the second image, where the surroundinginformation of one object is determined according to pixels within a setrange around a bounding box of the object in the image where the objectis located.

Both the first image and the second image in the embodiments include atleast one object. The object may be any object which may appear in areal scene. The type of the object is not limited in the embodiments.

In the embodiments, the first image may be acquired by a first imageacquisition device, and the second image may be acquired by a secondimage acquisition device. In some embodiments, the first imageacquisition device and the second image acquisition device may perform,in different views of a same scene, image acquisition on the scene so asto obtain the first image and the second image, respectively.Exemplarily, the first image and the second image may include the sameobjects; however, the positions of the object in the first image and inthe second image may be different. For example, the first image and thesecond image include the same background and three apples; however, thepositions of the three apples in the first image and in the second imageare different.

In some other embodiments, the first image and the second image mayinclude the same objects or objects which are different at least inpart. The positions of the object in the first image and in the secondimage are different. For example, the first image and the second imagehave the same background. However, the first image includes one appleand one pear, while the second image includes one pear and one orange.In this example, the first image and the second image include objectswhich are different at least in part.

Optionally, the surrounding information of the object includessurrounding pixels of the object in the image or features extracted forthe surrounding pixel of the object. For different images acquired indifferent views for the same scene, the surrounding information ofobjects having the same or similar appearance in the two images is alsoslightly different. On this basis, in the case that the objects in thefirst image and the second image have the same or similar appearance,the association relationship is determined according to the surroundinginformation of the objects in the two images in the process ofdetermining the association relationship between the objects in thefirst image and the second image.

The association relationship between two objects respectively located inthe two images represents that the two objects are associated with orunassociated with each other. The two objects respectively located intwo images being associated with each other represents that the twoobjects are the same object. The two objects respectively located in thetwo images being unassociated with each other represents that the twoobjects are not the same object. Exemplarily, the first image and thesecond image are images acquired in different views for the same scene;if the scene includes apple 1, apple 2, and apple 3, both the firstimage and the second image include apple 1, apple 2, and apple 3.Accordingly, apple 1 in the first image is associated with apple 1 inthe second image, apple 2 in the first image is associated with apple 2in the second image, and apple 3 in the first image is associated withapple 3 in the second image. Apple 1 in the first image is unassociatedwith apple 2 in the second image, apple 1 in the first image isunassociated with apple 3 in the second image, and so on.

In some optional embodiments of the present disclosure, determining theassociation relationship between the objects in the first image and theobjects in the second image based on the surrounding information of theobjects in the first image and the surrounding information of theobjects in the second image includes: determining the associationrelationship between the objects in the first image and the objects inthe second image based on the surrounding information and appearanceinformation of the objects in the first image, and the surroundinginformation and appearance information of the objects in the secondimage, where the appearance information of one object is determinedaccording to pixels within a bounding box of the object in the imagewhere the object is located.

In the embodiments, the appearance information of an object may includepixel information in a region where the object is located. In someembodiments, the region where the object is located may be labeled bymeans of a bounding box, and pixel information in the bounding box maybe taken as the appearance information. In some embodiments, thebounding boxes of the objects in each image may be labeled manually soas to obtain the appearance information of the objects in the firstimage and the second image. In some other embodiments, the images may beprocessed by means of a target detection network to obtain the boundingboxes of the objects in the images, and the pixel information in thebounding boxes of the objects in the images is taken as the appearanceinformation of the objects.

In the embodiments, after the bounding boxes of the objects in the firstimage and the second image are determined, the surrounding informationof the objects may be determined based on the bounding boxes of theobjects.

In some optional embodiments of the present disclosure, determining thesurrounding information of the objects includes: amplifying a regionwhere the bounding box of each object is located, and determining anamplified specific region, where the specific region is greater than theregion where the bounding box is located, and includes the region wherethe bounding box is located; and determining pixel information withinthe specific region and outside the bounding box as the surroundinginformation of the object.

In the embodiments, the region where the bounding box of each object islocated may be expanded according to a preset ratio. For example, foursides of the region where the bounding box is located are respectivelyexpanded by 20% of the corresponding side length to obtain the specificregion, and the pixel information corresponding to the region within thespecific region and outside the bounding box is taken as surroundingpixel information of one object.

In the embodiments, feature extraction is performed on the surroundinginformation and appearance information of an object in the first image,and feature extraction is performed on the surrounding information andappearance information of an object in the second image; matching isperformed on a feature of the surrounding information and a feature ofthe appearance information of one object in the first image and oneobject in the second image to determine a degree of similarity betweenone object in the first image and one object in the second image, andthe association relationship between the two images is determined basedon the degree of similarity.

In some optional embodiments of the present disclosure, as shown in FIG.2 , determining the association relationship between the objects in thefirst image and the objects in the second image based on the surroundinginformation and appearance information of the objects in the firstimage, and the surrounding information and appearance information of theobjects in the second image includes the following steps.

At step 201, first feature distances are determined based on theappearance information of the objects in the first image and theappearance information of the objects in the second image, where a firstfeature distance represents a degree of similarity between one object inthe first image and one object in the second image.

At step 202, second feature distances are determined based on thesurrounding information of the objects in the first image andsurrounding information of the objects in the second image, where asecond feature distance represents a degree of similarity between thesurrounding information of one object in the first image and thesurrounding information of one object in the second image.

At step 203, for one object in the first image and one object in thesecond image, a feature distance between the two objects is determinedaccording to the first feature distance and the second feature distanceof the two objects.

At step 204, the association relationship is determined between theobjects in the first image and the objects in the second image based onthe determined feature distance.

In the embodiments, feature extraction may be respectively performed onthe appearance information and surrounding information of the objects inthe first image and the second image by means of a feature extractionnetwork. First appearance features of the objects in the first image andsecond appearance features of the objects in the second image may berespectively obtained by performing feature extraction on the appearanceinformation. First surrounding features corresponding to the objects inthe first image and second surrounding features corresponding to theobjects in the second image may be respectively obtained by performingfeature extraction on the surrounding information. Exemplarily, thefeature extraction network includes one or more convolutional layers;convolution processing may be performed on the pixel information withinthe bounding box of each object in the first image and the second imageby means of the convolutional layers to obtain the first appearancefeature corresponding to each object in the first image and the secondappearance feature corresponding to each object in the second image, andconvolution processing may be performed on the pixel informationcorresponding to the surrounding information of the objects in the firstimage and the second image to obtain the first surrounding featurescorresponding to the objects in the first image and the secondsurrounding features corresponding to the objects in the second image.

In the embodiments, if any object in a first image is labeled as a firstobject, and any object in a second image is labeled as a second object,the first feature distance may be determined based on the firstappearance feature of the first object in the first image and the secondappearance feature of the second object in the second image. The firstfeature distance represents a degree of similarity between the firstobject and the second object. The larger the first feature distance is,it indicates that the lower the degree of similarity between the firstobject and the second object is. Accordingly, the smaller the firstfeature distance is, it indicates that the higher the degree ofsimilarity between the first object and the second object is. Inaddition, the second feature distance is determined based on a firstsurrounding feature corresponding to the first object and a secondsurrounding feature corresponding to the second object. The secondfeature distance represents a degree of similarity between thesurrounding information of the first object and the surroundinginformation of the second object. The larger the second feature distanceis, it indicates that the lower the degree of similarity between thesurrounding information of the first object and the surroundinginformation of the second object is. Accordingly, the smaller the secondfeature distance is, it indicates that the higher the degree ofsimilarity between the surrounding information of the first object andthe surrounding information of the second object is. Furthermore, thefeature distance between the first object and the second object may beobtained based on the first feature distance and the second featuredistance, and the association relationship between the first object andthe second object is determined based on the feature distance.

In some optional embodiments, an L2 distance may be calculated based onthe first appearance feature and the second appearance feature, and inthis case, the L2 distance is the first feature distance. Accordingly,the L2 distance may be calculated based on the first surrounding featureand the second surrounding feature, and in this case, the L2 distance isthe second feature distance.

Exemplarily, the L2 distance satisfies:

$\begin{matrix}{{d_{2}\left( {I_{1},I_{2}} \right)} = \sqrt{\sum\limits_{P}\left( {I_{1}^{P} - I_{2}^{P}} \right)^{2}}} & (1)\end{matrix}$

When the L2 distance is the first feature distance, I₁ and I₂respectively denote the first appearance feature and the secondappearance feature, and P denotes a dimension of the first appearancefeature and the second appearance feature; d₂ (I₁,I₂) denotes the L2distance between the first appearance feature and the second appearancefeature. Accordingly, when the L2 distance is the second featuredistance, I₁ and I₂ respectively denote the first surrounding featureand the second surrounding feature, and P denotes a dimension of thefirst surrounding feature and the second surrounding feature; d₂ (I₁,I₂)denotes the L2 distance between the first surrounding feature and thesecond surrounding feature.

In some optional embodiments, determining, according to the firstfeature distance and the second feature distance of the two objects, thefeature distance between the two objects includes: performing weightedsummation on the first feature distance and the second feature distanceof the two objects to obtain the feature distance between the twoobjects, where the higher the degree of similarity between the twoobjects, the greater a weight coefficient of the second feature distanceof the two objects during weighted summation.

In the embodiments, weighted summation processing may be performed onthe first feature distance and the second feature distance to obtain afeature distance between a first object and a second object. The largerthe feature distance is, it indicates that the lower an associationbetween the first object and the second object is. Accordingly, thesmaller the feature distance is, it indicates that the higher theassociation between the first object and the second object is. In someoptional embodiments, if the feature distance is greater than a firstpreset threshold, it can be determined that the first object isunassociated with the second object. If the feature distance is lessthan a second preset threshold, it can be determined that the firstobject is associated with the second object, where the second presetthreshold is less than the first preset threshold, and values of thefirst preset threshold and the second preset threshold may be determinedaccording to multiple experiments or tests.

In the embodiments, the higher a degree of similarity between the twoobjects, the greater a weight coefficient of a second feature distanceof the two objects during weighted summation. It can be understood thatthe two objects are more similar, it is more necessary to make referenceto the second feature distance between the surrounding information ofthe objects in the process of association matching between the objects.

In some optional embodiments, multiple weight coefficients may bepre-configured based on the difference in the degree of similaritybetween the first appearance feature and the second appearance feature,and one weight coefficient is selected from the multiple configuredweight coefficients according to the degree of similarity between thecurrent first appearance feature and second appearance feature as aweight coefficient of the second feature distance of the two objects.

In some other optional embodiments, the feature distance satisfies:

$\begin{matrix}{d^{ab} = {{\left( {1 - \lambda} \right) \times {D_{12}\left( {V_{app}^{a},V_{app}^{b}} \right)}} + {\lambda \times {D_{12}\left( {V_{sur}^{a},V_{sur}^{b}} \right)}}}} & (2)\end{matrix}$ $\begin{matrix}{\lambda = {S_{C}\left( {V_{app}^{a},V_{app}^{b}} \right)}} & (3)\end{matrix}$

where d^(ab) denotes a feature distance; k is a weight coefficient;D₁₂(V_(app) ^(a),V_(app) ^(b)) denotes the first feature distance (e.g.an L2 distance) between the first appearance feature V_(app) ^(a) andthe second appearance feature V_(app) ^(b); D₁₂(V_(sur) ^(a),V_(sur)^(b)) denotes the second feature distance (e.g. an L2 distance) betweenthe first surrounding feature V_(sur) ^(a) and the second surroundingfeature V_(sur) ^(b); S_(C) denotes a cosine similarity, i.e., theweight coefficient λ is obtained by calculating the cosine similaritybetween the first appearance feature V_(app) ^(a) and the secondappearance feature V_(app) ^(b).

For example, the above-mentioned process is shown in FIG. 3 , wheretaking two images recorded as View a and View b as example, View aincludes a bounding box corresponding to one object (labeled as a firstobject) and a region (labeled as a first specific region) correspondingto the surrounding information. View b includes a bounding boxcorresponding to one object (labeled as a second object) and a region(labeled as a second specific region) corresponding to the surroundinginformation. Pixel information of the bounding box and pixel informationof the first specific region of the first object are respectivelysegmented from View a and View b. In practical applications, for theobtaining of the region corresponding to the surrounding information, anamplified region of the region where each bounding box is located may besegmented from View a or View b, and then the pixel information of thefirst specific region and the second specific region is obtained bymeans of a mask having a size consistent with that of the bounding boxwithin a region range corresponding to the bounding boxes in thesegmented region.

Furthermore, feature extraction may be performed on the pixelinformation of the bounding box and the pixel information of specificregions (including the first specific region and the second specificregion) by means of two feature extractors, respectively. For example,feature extraction may be performed on the pixel information in thebounding boxes corresponding to View a and View b, and the pixelinformation of the specific regions (including the first specific regionand the second specific region) by means of an appearance featureextractor to obtain the first appearance feature V_(app) ^(a)corresponding to the first object and the second appearance featureV_(app) ^(b) corresponding to the second object. Feature extraction maybe performed on the pixel information of the first specific region ofView a and the second specific region of View b by means of asurrounding feature extractor to obtain the first surrounding featureV_(sur) ^(a) and the second surrounding feature V_(cur) ^(b). A cosinesimilarity between the first appearance feature V_(app) ^(a) and thesecond appearance feature V_(app) ^(b) is then calculated to obtain theweight coefficient λ. The L2 distance between the first appearancefeature V_(app) ^(a) and the second appearance feature V_(app) ^(b) iscalculated to obtain the feature distance d_(app) between the firstobject and the second object. The L2 distance between the firstsurrounding feature V_(sur) ^(a) and the second surrounding featureV_(sur) ^(b) is calculated to obtain the feature distance d_(sur)between the first surrounding feature and the second surroundingfeature. Finally, the feature distance between the first object and thesecond object is obtained by using the above-mentioned formula (2).

By using the technical solutions of the embodiments of the presentdisclosure, surrounding information of objects in different images istaken as the basis for association matching between the objects of thedifferent images, so that the association matching between objectshaving similar or same appearance in two images are achieved, and theprecision of association matching is improved.

The embodiments of the present disclosure also provide an objectassociation method. FIG. 4 is yet another schematic flowchart of anobject association method according to the embodiments of the presentdisclosure. As shown in FIG. 4 , the method includes the followingsteps.

At step 301, a first image and a second image are obtained.

At step 302, a first feature distance is determined based on appearanceinformation of objects in the first image and appearance information ofobjects in the second image, where a first feature distance represents adegree of similarity between one object in the first image and oneobject in the second image.

At step 303, second feature distances are determined based onsurrounding information of the objects in the first image andsurrounding information of the objects in the second image, where onesecond feature distance represents a degree of similarity betweensurrounding information of one object in the first image and surroundinginformation of one object in the second image.

At step 304, for one object in the first image and one object in thesecond image, a feature distance between the two objects is determinedaccording to the first feature distance and the second feature distanceof the two objects.

At step 305, geometric distance between the objects in the first imageand the objects in the second image are determined.

At step 306, for one object in the first image and one object in thesecond image, a distance between the two objects is determined accordingto the feature distance and the geometric distance between the twoobjects.

At step 307, an association relationship between the objects in thefirst image and the objects in the second image is determined accordingto the distances between the objects in the first image and the objectsin the second image.

For descriptions of step 301 to step 304 in the embodiments, referencemay be specifically made to the descriptions in the foregoingembodiments. Details are not described herein again.

In some optional embodiments of the present disclosure, determining thegeometric distances between the objects in the first image and theobjects in the second image includes: obtaining a first position of afirst image acquisition device which acquires the first image, and asecond position of a second image acquisition device which acquires thesecond image, and obtaining a first intrinsic parameter of the firstimage acquisition device and a second intrinsic parameter of the secondimage acquisition device; determining a third position of a center pointof one object in the first image in the first image; determining a polarline in the second image based on the first position, the secondposition, the third position, the first intrinsic parameter, and thesecond intrinsic parameter, where the polar line represents a straightline formed by projecting a connection line between a center point ofone object in the first image and an image point of the object in animaging plane of the first image acquisition device to the second image;determining a vertical pixel distance between one object in the secondimage and the polar line; and determining the geometric distancesbetween the objects in the first image and the objects in the secondimage according to determined vertical pixel distances.

In order to solve the problems of association matching between objectsin a scene where objects having the same or similar appearance andsimilar surrounding information are included in two images, the epipolargeometry mode is used for constraining in the embodiments so as toachieve the association matching between the objects in the scene, andimprove the accuracy of the association matching.

In the embodiments, the first image and the second image are imagesacquired in different views. Assuming that the first image correspondsto a first view, and the second image corresponds to a second view, fora first object in the first image, where the first object is any objectin the first image, a center point of the first object is projected to apoint P of an imaging plane of a first image acquisition device, and thecenter point and the point P are connected to form a straight line,which is a straight line in the first view corresponding to the firstimage. The straight line is projected to the second image to obtain apolar line in the second image in the second view. As shown in FIG. 5 ,assuming that the first object is an apple labeled by “x” in the leftimage of FIG. 5 , a connection line between a center point of the firstobject and an image point of the first object in an imaging plane of animage acquisition device which acquires the left image is projected tothe right image to obtain a polar line in the right image. The device atthe lower right corner of the right image is the image acquisitiondevice which acquires the left image.

In practical applications, a relative position relationship between thefirst image acquisition device and the second image acquisition devicemay be determined respectively according to a first position of thefirst image acquisition device and a second position of the second imageacquisition device. A conversion relationship may be determined based onthe relative position relationship, a first intrinsic parameter of thefirst image acquisition device, and a second intrinsic parameter of thesecond image acquisition device. Coordinates of a connection linebetween the center point of the first object and the third position inthe first image may be determined based on the third position of thecenter point of the first object in the first image. The coordinates ofthe connection line between the center point and the third position inthe first image are converted based on the conversion relationship toobtain coordinates of a polar line in the second image.

It can be understood that if the first object in the first image isassociated with the second object in the second image, that is, thefirst object and the second object are the same object, after the polarline in the second image is determined, a vertical pixel distancebetween the second object in the objects of the second image and thepolar line is smallest, or even 0. The vertical pixel distance refers toa geometric distance between two objects in the first image and thesecond image.

Therefore, which object in the second image is associated with the firstobject in the first image is determined by determining the verticalpixel distances between the objects in the second image and the polarline.

In some optional embodiments of the present disclosure, determining,according to the feature distance and the geometric distance between thetwo objects, the distance between the two objects includes: performingweighted summation on the feature distance and the geometric distancebetween the two objects to obtain the distance between the two objects.

In the embodiments, the distance between each pair of first object andsecond object is obtained by performing weighted summation processing onthe feature distance and the geometric distance. A fixed preset valuemay be used as a weight coefficient used in the weighted summationprocessing. The value of the weight coefficient is not limited in theembodiments.

In some optional embodiments of the present disclosure, determining theassociation relationship between the objects in the first image and theobjects in the second image according to the distances between theobjects in the first image and the objects in the second image includes:forming a distance matrix based on the distances between the objects inthe first image and the objects in the second image, where a value ofone element in the distance matrix represents a distance between oneobject in the first image and one object in the second image; anddetermining an adjacency matrix between the first image and the secondimage according to the distance matrix, where a value of an element inthe adjacency matrix represents that one object in the first image isassociated with or unassociated with one object in the second image.

In the embodiments, one distance is obtained for each pair of firstobject and second object accordingly. Therefore, there may be M×Ndistances between M objects in the first image and N objects in thesecond image. Accordingly, an M×N distance matrix may be formed. Asshown in FIG. 6 , assuming that View 1 and View 2 include three objects,respectively, a 3×3 distance matrix may be formed. The distance matrixis processed according to a preset algorithm to obtain an adjacencymatrix. One pair of objects in the adjacency matrix having a value of 1are associated with each other, and one pair of objects in the adjacencymatrix having a value of 0 are unassociated with each other.Exemplarily, the distance matrix may be processed according toKuhn-Munkres (KM) algorithm to obtain an adjacency matrix.

The embodiments of the present disclosure also provide an objectassociation apparatus. FIG. 7 is a schematic structural diagram ofcomponents of an object association apparatus according to theembodiments of the present disclosure. As shown in FIG. 7 , theapparatus includes: an obtaining unit 31 and a determination unit 32,where

the obtaining unit 31 is configured to obtain a first image and a secondimage; and

the determination unit 32 is configured to determine an associationrelationship between objects in the first image and objects in thesecond image based on surrounding information of the objects in thefirst image and surrounding information of the objects in the secondimage, where the surrounding information of one object is determinedaccording to pixels within a set range around a bounding box of theobject in the image where the object is located.

In some optional embodiments of the present disclosure, thedetermination unit 32 is configured to determine the associationrelationship between the objects in the first image and the objects inthe second image based on the surrounding information and appearanceinformation of the objects in the first image, and the surroundinginformation and appearance information of the objects in the secondimage, where the appearance information of one object is determinedaccording to pixels within a bounding box of the object in the imagewhere the object is located.

In some optional embodiments of the present disclosure, thedetermination unit 32 is configured to determine first feature distancesbased on the appearance information of the objects in the first imageand the appearance information of the objects in the second image, wherea first feature distance represents a degree of similarity between oneobject in the first image and one object in the second image; determinesecond feature distances based on the surrounding information of theobjects in the first image and the surrounding information of theobjects in the second image, where a second feature distance representsa degree of similarity between the surrounding information of one objectin the first image and the surrounding information of one object in thesecond image; for one object in the first image and one object in thesecond image, determine, according to the first feature distance and thesecond feature distance of the two objects, a feature distance betweenthe two objects; and determine the association relationship between theobjects in the first image and the objects in the second image based onthe determined feature distance.

In some optional embodiments of the present disclosure, thedetermination unit 32 is configured to perform weighted summation on thefirst feature distance and the second feature distance of the twoobjects to obtain the feature distance between the two objects, wherethe higher the degree of similarity between the two objects, the greatera weight coefficient of the second feature distance of the two objectsduring weighted summation.

In some optional embodiments of the present disclosure, thedetermination unit 32 is further configured to determine geometricdistances between the objects in the first image and the objects in thesecond image, and is further configured to: for one object in the firstimage and one object in the second image, determine, according to thefeature distance and the geometric distance between the two objects, adistance between the two objects; and determine the associationrelationship between the objects in the first image and the objects inthe second image according to the distances between the objects in thefirst image and the objects in the second image.

In some optional embodiments of the present disclosure, thedetermination unit 32 is configured to: obtain a first position of afirst image acquisition device which acquires the first image, and asecond position of a second image acquisition device which acquires thesecond image, and obtain a first intrinsic parameter of the first imageacquisition device and a second intrinsic parameter of the second imageacquisition device; determine a third position of a center point of oneobject in the first image in the first image; determine a polar line inthe second image based on the first position, the second position, thethird position, the first intrinsic parameter, and the second intrinsicparameter, where the polar line represents a straight line formed byprojecting a connection line between a center point of one object in thefirst image and an image point of the object in an imaging plane of thefirst image acquisition device to the second image; determine a verticalpixel distance between one object in the second image and the polarline; and determine the geometric distances between the objects in thefirst image and the objects in the second image according to thedetermined vertical pixel distance.

In some optional embodiments of the present disclosure, thedetermination unit 32 is configured to perform weighted summation on thefeature distance and the geometric distance between the two objects toobtain the distance between the two objects.

In some optional embodiments of the present disclosure, thedetermination unit 32 is configured to form a distance matrix based onthe distances between the objects in the first image and the objects inthe second image, where a value of one element in the distance matrixrepresents a distance between one object in the first image and oneobject in the second image; and determine an adjacency matrix betweenthe first image and the second image according to the distance matrix,where a value of an element in the adjacency matrix represents that oneobject in the first image is associated with or unassociated with oneobject in the second image.

In the embodiments of the present disclosure, the obtaining unit 31 andthe determination unit 32 in the object association apparatus may bothbe implemented by a Center Processing Unit (CPU), a Digital SignalProcessor (DSP), a Microcontroller Unit (MCU), or a Field-ProgrammableGate Array (FPGA) in practical applications.

It should be noted that: when performing object association processing,the object association apparatus provided by the aforementionedembodiments are exemplified by division of the above-mentioned proceduremodules. In practical applications, the processing allocations above maybe achieved by different procedure modules as needed. That is, theinternal structure of the apparatus is divided into different proceduremodules to achieve all or some of the processing described above. Inaddition, the object association apparatus provided by theaforementioned embodiments and the object association method embodimentbelong to the same concept. Please refer to the method embodiments forthe specific implementation process of the object association apparatus.Details are not described herein again.

The embodiments of the present disclosure also provide an electronicdevice. FIG. 8 is a schematic structural diagram of hardware of anelectronic device according to the embodiments of the presentdisclosure. As shown in FIG. 8 , the electronic device 40 includes amemory 42, a processor 41, and a computer program stored on the memory42 and executable on the processor 41, where when the processor 41executes the program, the steps of the image processing method accordingto the embodiments of the present disclosure are implemented.

The components in the electronic device 40 are coupled together througha bus system 43. It can be understood that the bus system 43 isconfigured to implement connection and communication between thecomponents. In addition to a data bus, the bus system 43 furtherincludes a power bus, a control bus, and a status signal bus. However,for clarity, all the buses are labeled as the bus system 43 in FIG. 8 .

It can be understood that the memory 42 may be a volatile memory or anon-volatile memory, or may also include both a volatile memory and anon-volatile memory. The non-volatile memory may be a Read-Only Memory(ROM), a Programmable Read-Only Memory (PROM), an Erasable ProgrammableRead-Only Memory (EPROM), an Electrically Erasable ProgrammableRead-Only Memory (EEPROM), a Ferromagnetic Random Access Memory (FRAM),a flash memory, a magnetic surface memory, an optical disk, or a CompactDisc Read-Only Memory (CD-ROM). The magnetic surface memory may be amagnetic-disk memory or a magnetic tape memory. The volatile memory maybe a Random Access Memory (RAM), which acts as an external cache. By wayof exemplary but not restrictive descriptions, many forms of RAMs areavailable, such as Static Random Access Memory (SRAM), SynchronousStatic Random Access Memory (SSRAM), Dynamic Random Access Memory(DRAM), Synchronous Dynamic Random Access Memory (SDRAM), Double DataRate Synchronous Dynamic Random Access Memory (DDRSDRAM), EnhancedSynchronous Dynamic Random Access Memory (ESDRAM), SyncLink DynamicRandom Access Memory (SLDRAM), and Direct Rambus Random Access Memory(DRRAM). The memory 42 described in the embodiments of the presentdisclosure is aimed at including, but not limited to, these and anyother suitable type of memory.

The method disclosed by the aforementioned embodiments of the presentdisclosure can be applied to the processor 41, or is implemented by theprocessor 41. The processor 41 may be an integrated circuit chip and hasa signal processing capability. During implementation, the steps of theforegoing method may be completed by means of an integrated logiccircuit of hardware in the processor 41 or instructions in the form ofsoftware. The processor 41 may be a general-purpose processor, a DSP, orother programmable logic device, discrete gate or transistor logicdevice, discrete hardware component or the like. The processor 41 canimplement or execute the methods, the steps, and the logical blockdiagrams disclosed in the embodiments of the present disclosure. Thegeneral-purpose processor may be a microprocessor or any conventionalprocessor. The steps of the method disclosed with reference to theembodiments of the present disclosure may be directly implemented by ahardware decoding processor, or implemented by a combination of hardwareand software modules in a decoding processor. The software module may belocated in a storage medium, and the storage medium is located in thememory 42. The processor 41 reads information in the memory 42 andimplements the steps of the foregoing method in combination with thehardware thereof.

In an exemplary embodiment, the electronic device 40 may be implementedby one or more Application Specific Integrated Circuits (ASICs), a DSP,a Programmable Logic Device (PLD), a Complex Programmable Logic Device(CPLD), an FPGA, a general-purpose processor, a controller, an MCU, amicroprocessor, or other electronic elements, to perform the foregoingmethod.

In an exemplary embodiment, the embodiments of the present disclosurefurther provide a computer readable storage medium, for example, thememory 42 including the computer program. The computer program isexecuted by the processor 41 in the electronic device 40 to implementthe steps of the foregoing method. The computer readable storage mediummay be a memory such as FRAM, ROM, PROM, EPROM, EEPROM, a flash memory,a magnetic surface memory, an optical disk, or CD-ROM, and may also beany device including one or any combination of the aforementionedmemories, such as a mobile phone, a computer, a tablet device, or apersonal digital assistant.

The embodiments of the present disclosure also provide an objectassociation system.

The system includes:

a first image acquisition device, configured to acquire one scene in afirst view to obtain a first image;

a second image acquisition device configured to acquire the scene in asecond view to obtain a second image, where the first view is differentfrom the second view; and

a processor configured to: obtain the first image and the second image;and determine an association relationship between objects in the firstimage and objects in the second image based on surrounding informationof the objects in the first image and surrounding information of theobjects in the second image, where the surrounding information of oneobject is determined according to pixels within a set range around abounding box of the object in the image where the object is located.

In some optional embodiments of the present disclosure, the processor isconfigured to determine an association relationship between the objectsin the first image and the objects in the second image based on thesurrounding information and appearance information of the objects in thefirst image, and the surrounding information and appearance informationof the objects in the second image, where the appearance information ofone object is determined according to pixels within a bounding box ofthe object in the image where the object is located.

In some optional embodiments of the present disclosure, the processor isconfigured to determine first feature distances based on the appearanceinformation of the objects in the first image and the appearanceinformation of the objects in the second image, where a first featuredistance represents a degree of similarity between one object in thefirst image and one object in the second image; determine second featuredistances based on the surrounding information of the objects in thefirst image and the surrounding information of the objects in the secondimage, where a second feature distance represents a degree of similaritybetween the surrounding information of one object in the first image andthe surrounding information of one object in the second image; for oneobject in the first image and one object in the second image, determine,according to the first feature distance and the second feature distanceof the two objects, a feature distance between the two objects; anddetermine the association relationship between the objects in the firstimage and the objects in the second image based on the determinedfeature distance.

In some optional embodiments of the present disclosure, the processor isconfigured to perform weighted summation on the first feature distanceand the second feature distance of the two objects to obtain the featuredistance between the two objects, where the higher the degree ofsimilarity between the two objects, the greater a weight coefficient ofthe second feature distance of the two objects during weightedsummation.

In some optional embodiments of the present disclosure, the processor isfurther configured to: determine geometric distances between the objectsin the first image and the objects in the second image; and for oneobject in the first image and one object in the second image, determine,according to the feature distance and the geometric distance between thetwo objects, a distance between the two objects; and determine anassociation relationship between the objects in the first image and theobjects in the second image according to the distances between theobjects in the first image and the objects in the second image.

In some optional embodiments of the present disclosure, the processor isconfigured to: obtain a first position of a first image acquisitiondevice which acquires the first image, and a second of where a secondimage acquisition device which acquires the second image, and obtain afirst intrinsic parameter of the first image acquisition device and asecond intrinsic parameter of the second image acquisition device;determine a third position of a center point of one object in the firstimage in the first image; determine a polar line in the second imagebased on the first position, the second position, the third position,the first intrinsic parameter, and the second intrinsic parameter, wherethe polar line represents a straight line formed by projecting aconnection line between a center point of one object in the first imageand an image point of the object in an imaging plane of the first imageacquisition device to the second image; determine the vertical pixeldistance between one object in the second image and the polar line; anddetermine the geometric distances between the objects in the first imageand the objects in the second image according to the determined verticalpixel distance.

In some optional embodiments of the present disclosure, the processor isconfigured to perform weighted summation on the feature distance and thegeometric distance between the two objects to obtain the distancebetween the two objects.

In some optional embodiments of the present disclosure, the processor isconfigured to: form a distance matrix based on the distances between theobjects in the first image and the objects in the second image, where avalue of one element in the distance matrix represents a distancebetween one object in the first image and one object in the secondimage; and determine an adjacency matrix between the first image and thesecond image according to the distance matrix, where a value of anelement in the adjacency matrix represents that one object in the firstimage is associated with or unassociated with one object in the secondimage.

The computer readable storage medium provided by the embodiments of thepresent disclosure has a computer program stored thereon, where when theprogram is executed by a processor, the steps of the image processingmethod according to the foregoing embodiments of the present disclosureare implemented.

The methods disclosed in the method embodiments provided by the presentdisclosure can be arbitrarily combined without causing conflicts so asto obtain a new method embodiment.

The features disclosed in several product embodiments provided by thepresent disclosure can be arbitrarily combined without causing conflictsso as to obtain a new product embodiment.

The features disclosed in several method or device embodiments providedby the present disclosure can be arbitrarily combined without causingconflicts so as to obtain a new method or device embodiment.

It should be understood that the disclosed device and method in severalembodiments provided in the present disclosure may be implemented inother manners. The device embodiments described above are merelyexemplary. For example, the unit division is merely logical functiondivision and may be actually implemented in other division manners. Forexample, a plurality of units or components may be combined orintegrated into another system, or some features may be ignored or notperformed. In addition, the displayed or discussed mutual couplings ordirect couplings or communication connections among the components maybe implemented by means of some ports. The indirect couplings orcommunication connections between the devices or units may beelectrical, mechanical, or in other forms.

The units described as separate components may or may not be physicallyseparate, and the components displayed as units may or may not bephysical units, i.e., may be located at one position, or may bedistributed on a plurality of network units. Some or all of the unitsmay be selected according to actual needs to achieve the objectives ofthe solutions of the embodiments.

In addition, the functional units in the embodiments of the presentdisclosure may be integrated into one processing unit, or each of theunits may exist as one independent unit, or two or more units areintegrated into one unit, and the integrated unit may be implemented inthe form of hardware, or may be implemented in the form of hardware andsoftware functional units.

A person of ordinary skill in the art may understand that all or somesteps for implementing the foregoing method embodiments may be achievedby a program by instructing related hardware; the foregoing program canbe stored in a computer readable storage medium; when the program isexecuted, the steps in the foregoing method embodiments are performed.Moreover, the foregoing storage medium includes various media capable ofstoring a program code, such as a mobile storage device, a ROM, a RAM, amagnetic disk, or an optical disk.

Alternatively, when the foregoing integrated unit of the presentdisclosure is implemented in the form of a software functional moduleand sold or used as an independent product, the integrated unit may bestored in one computer readable storage medium. Based on such anunderstanding, the technical solutions in the embodiments of the presentdisclosure or some contributing to the prior art may be essentiallyembodied in the form of software products. The computer software productis stored in one storage medium and includes several instructions sothat one computer device (which may be a personal computer, a server, anetwork device, and the like) implements all or a part of the method inthe embodiments of the present disclosure. Moreover, the storage mediumabove includes various media capable of storing a program code, such asa mobile storage device, a ROM, a RAM, a magnetic disk, or an opticaldisk.

The descriptions above are only specific implementations of the presentdisclosure. However, the scope of protection of the present disclosureis not limited thereto. Within the technical scope disclosed by thepresent disclosure, any variation or substitution that can be easilyconceived of by those skilled in the art should all fall within thescope of protection of the present disclosure. Therefore, the scope ofprotection of the present disclosure should be determined by the scopeof protection of the appended claims.

The invention claimed is:
 1. An object association method, comprising:obtaining a first image and a second image; and determining a pluralityof first feature distances based on appearance information of aplurality of objects in the first image and appearance information of aplurality of objects in the second image, wherein a first featuredistance represents a degree of similarity between one object of theplurality of objects in the first image and one object of the pluralityof objects in the second image, and appearance information of one objectis determined according to pixels within a bounding box of the object inan image where the object is located; determining a plurality of secondfeature distances based on surrounding information of the plurality ofobjects in the first image and surrounding information of the pluralityof objects in the second image, wherein a second feature distancerepresents a degree of similarity between surrounding information of oneobject of the plurality of objects in the first image and surroundinginformation of one object of the plurality of objects in the secondimage, and surrounding information of one object is determined accordingto pixels within a set range around a bounding box of the object in animage where the object is located; for one object in the first image andone object in the second image, performing weighted summation on a firstfeature distance and a second feature distance between the object in thefirst image and the object in the second image to obtain a featuredistance between the object in the first image and the object in thesecond image, wherein a plurality of weight coefficients arepre-configured, and a weight coefficient is selected from the pluralityof weight coefficients according to the first feature distance betweenthe object in the first image and the object in the second image as aweight coefficient of the second feature distance, and in condition thatthe first feature distance between the object in the first image and theobject in the second image is smaller, the weight coefficient of thesecond feature distance between the object in the first image and theobject in the second image is larger during weighted summation; anddetermining an association relationship between the plurality of objectsin the first image and the plurality of objects in the second imagebased on a plurality of determined feature distances.
 2. The methodaccording to claim 1, wherein the method further comprises: determininga plurality of geometric distances between the plurality of objects inthe first image and the plurality of objects in the second image; anddetermining the association relationship between the plurality ofobjects in the first image and the plurality of objects in the secondimage based on the plurality of determined feature distances comprises:for one object in the first image and one object in the second image,determining, according to a feature distance and a geometric distancebetween the object in the first image and the object in the secondimage, a distance between the object in the first image and the objectin the second image; and determining the association relationshipbetween the plurality of objects in the first image and the plurality ofobjects in the second image according to a plurality of distancesbetween the plurality of objects in the first image and the plurality ofobjects in the second image.
 3. The method according to claim 2, whereindetermining the plurality of geometric distances between the pluralityof objects in the first image and the plurality of objects in the secondimage comprises: obtaining a first position of a first image acquisitiondevice which acquires the first image, and a second position of a secondimage acquisition device which acquires the second image, and obtaininga first intrinsic parameter of the first image acquisition device and asecond intrinsic parameter of the second image acquisition device;determining a third position of a center point of one object in thefirst image; determining a polar line in the second image based on thefirst position, the second position, the third position, the firstintrinsic parameter, and the second intrinsic parameter, wherein thepolar line represents a straight line formed by projecting a connectionline between a center point of one object in the first image and animage point of the object in an imaging plane of the first imageacquisition device to the second image; determining a vertical pixeldistance between one object in the second image and the polar line; anddetermining the plurality of geometric distances between the pluralityof objects in the first image and the plurality of objects in the secondimage according to a plurality of determined vertical pixel distances.4. The method according to claim 2, wherein determining, according tothe feature distance and the geometric distance between the object inthe first image and the object in the second image, the distance betweenthe object in the first image and the object in the second imagecomprises: performing weighted summation on the feature distance and thegeometric distance between the object in the first image and the objectin the second image to obtain the distance between the object in thefirst image and the object in the second image.
 5. The method accordingto claim 2, wherein determining the association relationship between theplurality of objects in the first image and the plurality of objects inthe second image according to the plurality of distances between theplurality of objects in the first image and the plurality of objects inthe second image comprises: forming a distance matrix based on theplurality of distances between the plurality of objects in the firstimage and the plurality of objects in the second image, wherein a valueof one element in the distance matrix represents a distance between oneobject in the first image and one object in the second image; anddetermining an adjacency matrix between the first image and the secondimage according to the distance matrix, wherein a value of an element inthe adjacency matrix represents that one object in the first image isassociated with or unassociated with one object in the second image. 6.An object association system, comprising: a first image acquisitiondevice, configured to acquire one scene at a first view to obtain thefirst image; a second image acquisition device, configured to acquirethe scene at a second view to obtain the second image, wherein the firstview is different from the second view; and a processor, configured toperform the object association method according to claim
 1. 7. Themethod according to claim 1, wherein determining the associationrelationship between the plurality of objects in the first image and theplurality of objects in the second image based on the plurality ofdetermined feature distances comprises: when the determined featuredistance is greater than a first preset threshold, determining that theobject in the first image is unassociated with the object in the secondimage; and when the determined feature distance is less than a secondpreset threshold, determining that the object in the first image isassociated with the object in the second image, wherein the secondpreset threshold is less than the first preset threshold.
 8. An objectassociation apparatus, comprising: a processor; and a memory configuredto store computer instructions executable by the processor, wherein theprocessor is configured to: obtain a first image and a second image;determine a plurality of first feature distances based on appearanceinformation of a plurality of objects in the first image and appearanceinformation of a plurality of objects in the second image, wherein afirst feature distance represents a degree of similarity between oneobject of the plurality of objects in the first image and one object ofthe plurality of objects in the second image, and appearance informationof one object is determined according to pixels within a bounding box ofthe object in an image where the object is located; determine aplurality of second feature distances based on surrounding informationof the plurality of objects in the first image and surroundinginformation of the plurality of objects in the second image, wherein asecond feature distance represents a degree of similarity betweensurrounding information of one object of the plurality of objects in thefirst image and surrounding information of one object of the pluralityof objects in the second image, and surrounding information of oneobject is determined according to pixels within a set range around abounding box of the object in an image where the object is located; forone object in the first image and one object in the second image,perform weighted summation on a first feature distance and a secondfeature distance between the object in the first image and the object inthe second image to obtain a feature distance between the object in thefirst image and the object in the second image, wherein a plurality ofweight coefficients are pre-configured, and a weight coefficient isselected from the plurality of weight coefficients according to thefirst feature distance between the object in the first image and theobject in the second image as a weight coefficient of the second featuredistance, and in condition that the first feature distance between theobject in the first image and the object in the second image is smaller,the weight coefficient of the second feature distance between the objectin the first image and the object in the second image is larger duringweighted summation; and determine an association relationship betweenthe plurality of objects in the first image and the plurality of objectsin the second image based on a plurality of determined featuredistances.
 9. The apparatus according to claim 8, wherein the processoris further configured to: determine a plurality of geometric distancesbetween the plurality of objects in the first image and the plurality ofobjects in the second image, and for one object in the first image andone object in the second image, determine, according to a featuredistance and a geometric distance between the object in the first imageand the object in the second image, a distance between the object in thefirst image and the object in the second image; and determine theassociation relationship between the plurality of objects in the firstimage and the plurality of objects in the second image according to aplurality of distances between the plurality of objects in the firstimage and the plurality of objects in the second image.
 10. Theapparatus according to claim 9, wherein the processor is configured to:obtain a first position of a first image acquisition device whichacquires the first image, and a second position of a second imageacquisition device which acquires the second image, and obtain a firstintrinsic parameter of the first image acquisition device and a secondintrinsic parameter of the second image acquisition device; determine athird position of a center point of one object in the first image;determine a polar line in the second image based on the first position,the second position, the third position, the first intrinsic parameter,and the second intrinsic parameter, wherein the polar line represents astraight line formed by projecting a connection line between a centerpoint of one object in the first image and an image point of the objectin an imaging plane of the first image acquisition device to the secondimage; determine a vertical pixel distance between one object in thesecond image and the polar line; and determine the plurality ofgeometric distances between the plurality of objects in the first imageand the plurality of objects in the second image according to aplurality of determined vertical pixel distances.
 11. The apparatusaccording to claim 9, wherein the processor is configured to performweighted summation on the feature distance and the geometric distancebetween the object in the first image and the object in the second imageto obtain the distance between the object in the first image and theobject in the second image.
 12. The apparatus according to claim 9,wherein the processor is configured to: form a distance matrix based onthe plurality of distances between the plurality of objects in the firstimage and the plurality of objects in the second image, wherein a valueof one element in the distance matrix represents a distance between oneobject in the first image and one object in the second image; anddetermine an adjacency matrix between the first image and the secondimage according to the distance matrix, wherein a value of an element inthe adjacency matrix represents that one object in the first image isassociated with or unassociated with one object in the second image. 13.The apparatus according to claim 8, wherein the processor is furtherconfigured to: when the determined feature distance is greater than afirst preset threshold, determine that the object in the first image isunassociated with the object in the second image; and when thedetermined feature distance is less than a second preset threshold,determine that the object in the first image is associated with theobject in the second image, wherein the second preset threshold is lessthan the first preset threshold.
 14. A non-transitory computer readablestorage medium, having a computer program stored thereon, wherein thecomputer program, when being executed by a processor, enables theprocessor to implement the operations of: obtaining a first image and asecond image; determining a plurality of first feature distances basedon appearance information of a plurality of objects in the first imageand appearance information of a plurality of objects in the secondimage, wherein a first feature distance represents a degree ofsimilarity between one object of the plurality of objects in the firstimage and one object of the plurality of objects in the second image,and appearance information of one object is determined according topixels within a bounding box of the object in an image where the objectis located; determining a plurality of second feature distances based onsurrounding information of the plurality of objects in the first imageand surrounding information of the plurality of objects in the secondimage, wherein a second feature distance represents a degree ofsimilarity between surrounding information of one object of theplurality of objects in the first image and surrounding information ofone object of the plurality of objects in the second image, andsurrounding information of one object is determined according to pixelswithin a set range around a bounding box of the object in an image wherethe object is located; for one object in the first image and one objectin the second image, performing weighted summation on a first featuredistance and a second feature distance between the object in the firstimage and the object in the second image to obtain a feature distancebetween the object in the first image and the object in the secondimage, wherein a plurality of weight coefficients are pre-configured,and a weight coefficient is selected from the plurality of weightcoefficients according to the first feature distance between the objectin the first image and the object in the second image as a weightcoefficient of the second feature distance, and in condition that thefirst feature distance between the object in the first image and theobject in the second image is smaller, the weight coefficient of thesecond feature distance between the object in the first image and theobject in the second image is larger during weighted summation; anddetermining an association relationship between the plurality of objectsin the first image and the plurality of objects in the second imagebased on a plurality of determined feature distances.
 15. Thenon-transitory computer readable storage medium according to claim 14,wherein the computer program, when being executed by the processor,enables the processor to further implement the operations of: when thedetermined feature distance is greater than a first preset threshold,determining that the object in the first image is unassociated with theobject in the second image; and when the determined feature distance isless than a second preset threshold, determining that the object in thefirst image is associated with the object in the second image, whereinthe second preset threshold is less than the first preset threshold.